




'''simple docstring'''


import inspect
import unittest

import numpy as np

from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor


if is_flax_available():
  import jax

  from transformers import FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling, FlaxBeitModel

if is_vision_available():
  from PIL import Image

  from transformers import BeitImageProcessor





class  __lowercase    (	unittest.TestCase			):







   def __init__(						self   ,				UpperCamelCase   ,				UpperCamelCase=100   ,				UpperCamelCase=13   ,				UpperCamelCase=30   ,				UpperCamelCase=2   ,				UpperCamelCase=3   ,				UpperCamelCase=True   ,				UpperCamelCase=True   ,				UpperCamelCase=32   ,				UpperCamelCase=5   ,				UpperCamelCase=4   ,				UpperCamelCase=37   ,				UpperCamelCase="gelu"   ,				UpperCamelCase=0.1   ,				UpperCamelCase=0.1   ,				UpperCamelCase=10   ,				UpperCamelCase=0.02   ,				UpperCamelCase=3   ,				)					->     Tuple:
     __a								=       parent
     __a								=       vocab_size
     __a								=       batch_size
     __a								=       image_size
     __a								=       patch_size
     __a								=       num_channels
     __a								=       is_training
     __a								=       use_labels
     __a								=       hidden_size
     __a								=       num_hidden_layers
     __a								=       num_attention_heads
     __a								=       intermediate_size
     __a								=       hidden_act
     __a								=       hidden_dropout_prob
     __a								=       attention_probs_dropout_prob
     __a								=       type_sequence_label_size
     __a								=       initializer_range

     # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
     __a								=       (image_size // patch_size) ** 2
     __a								=       num_patches + 1







   def 		UpperCamelCase__      (						self   )					->     Any:
     __a								=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

     __a								=       None
     if self.use_labels:
       __a								=       ids_tensor([self.batch_size]   ,				self.type_sequence_label_size   )

     __a								=       BeitConfig(
         vocab_size=self.vocab_size   ,				image_size=self.image_size   ,				patch_size=self.patch_size   ,				num_channels=self.num_channels   ,				hidden_size=self.hidden_size   ,				num_hidden_layers=self.num_hidden_layers   ,				num_attention_heads=self.num_attention_heads   ,				intermediate_size=self.intermediate_size   ,				hidden_act=self.hidden_act   ,				hidden_dropout_prob=self.hidden_dropout_prob   ,				attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,				is_decoder=UpperCamelCase   ,				initializer_range=self.initializer_range   ,				)

     return config, pixel_values, labels







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   )					->     List[Any]:
     __a								=       FlaxBeitModel(config=UpperCamelCase   )
     __a								=       model(UpperCamelCase   )
     self.parent.assertEqual(result.last_hidden_state.shape   ,				(self.batch_size, self.seq_length, self.hidden_size)   )







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   )					->     Tuple:
     __a								=       FlaxBeitForMaskedImageModeling(config=UpperCamelCase   )
     __a								=       model(UpperCamelCase   )
     self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.seq_length - 1, self.vocab_size)   )







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   )					->     List[str]:
     __a								=       self.type_sequence_label_size
     __a								=       FlaxBeitForImageClassification(config=UpperCamelCase   )
     __a								=       model(UpperCamelCase   )
     self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.type_sequence_label_size)   )

     # test greyscale images
     __a								=       1
     __a								=       FlaxBeitForImageClassification(UpperCamelCase   )

     __a								=       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]   )
     __a								=       model(UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     Optional[int]:
     __a								=       self.prepare_config_and_inputs()
     (
         (
         __a
     )						,							(
         __a
     )						,							(
         __a
     )						,							
     )								=       config_and_inputs
     __a								=       {'pixel_values': pixel_values}
     return config, inputs_dict





@require_flax
class  __lowercase    (	__magic_name__							,				unittest.TestCase			):
   _a    						=   (
       (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
   )







   def 		UpperCamelCase__      (						self   )					->     None:
     __a								=       FlaxBeitModelTester(self   )
     __a								=       ConfigTester(self   ,				config_class=UpperCamelCase   ,				has_text_modality=UpperCamelCase   ,				hidden_size=37   )







   def 		UpperCamelCase__      (						self   )					->     int:
     self.config_tester.run_common_tests()







   def 		UpperCamelCase__      (						self   )					->     Optional[int]:
     __a						,							__a								=       self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       __a								=       model_class(UpperCamelCase   )
       __a								=       inspect.signature(model.__call__   )
       # signature.parameters is an OrderedDict => so arg_names order is deterministic
       __a								=       [*signature.parameters.keys()]

       __a								=       ['pixel_values']
       self.assertListEqual(arg_names[:1]   ,				UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     Optional[int]:
     __a						,							__a								=       self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       with self.subTest(model_class.__name__   ):
         __a								=       self._prepare_for_class(UpperCamelCase   ,				UpperCamelCase   )
         __a								=       model_class(UpperCamelCase   )

         @jax.jit
         def model_jitted(UpperCamelCase   ,				**UpperCamelCase   ):
           return model(pixel_values=UpperCamelCase   ,				**UpperCamelCase   )

         with self.subTest('JIT Enabled'   ):
           __a								=       model_jitted(**UpperCamelCase   ).to_tuple()

         with self.subTest('JIT Disabled'   ):
           with jax.disable_jit():
             __a								=       model_jitted(**UpperCamelCase   ).to_tuple()

         self.assertEqual(len(UpperCamelCase   )   ,				len(UpperCamelCase   )   )
         for jitted_output, output in zip(UpperCamelCase   ,				UpperCamelCase   ):
           self.assertEqual(jitted_output.shape   ,				output.shape   )







   def 		UpperCamelCase__      (						self   )					->     Any:
     __a								=       self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     Union[str, Any]:
     __a								=       self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_masked_lm(*UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     Optional[Any]:
     __a								=       self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*UpperCamelCase   )







   @slow
   def 		UpperCamelCase__      (						self   )					->     Union[str, Any]:
     for model_class_name in self.all_model_classes:
       __a								=       model_class_name.from_pretrained('microsoft/beit-base-patch16-224'   )
       __a								=       model(np.ones((1, 3, 224, 224)   )   )
       self.assertIsNotNone(UpperCamelCase   )


def     SCREAMING_SNAKE_CASE							(   ):
  __a								=       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
  return image





@require_vision
@require_flax
class  __lowercase    (	unittest.TestCase			):







   @cached_property
   def 		UpperCamelCase__      (						self   )					->     Any:
     return BeitImageProcessor.from_pretrained('microsoft/beit-base-patch16-224'   ) if is_vision_available() else None







   @slow
   def 		UpperCamelCase__      (						self   )					->     Union[str, Any]:
     __a								=       FlaxBeitForMaskedImageModeling.from_pretrained('microsoft/beit-base-patch16-224-pt22k'   )

     __a								=       self.default_image_processor
     __a								=       prepare_img()
     __a								=       image_processor(images=UpperCamelCase   ,				return_tensors='np'   ).pixel_values

     # prepare bool_masked_pos
     __a								=       np.ones((1, 196)   ,				dtype=UpperCamelCase   )

     # forward pass
     __a								=       model(pixel_values=UpperCamelCase   ,				bool_masked_pos=UpperCamelCase   )
     __a								=       outputs.logits

     # verify the logits
     __a								=       (1, 196, 8192)
     self.assertEqual(logits.shape   ,				UpperCamelCase   )

     __a								=       np.array(
         [[-3.2_437, 0.5_072, -13.9_174], [-3.2_456, 0.4_948, -13.9_401], [-3.2_033, 0.5_121, -13.8_550]]   )

     self.assertTrue(np.allclose(logits[bool_masked_pos][:3, :3]   ,				UpperCamelCase   ,				atol=1e-2   )   )







   @slow
   def 		UpperCamelCase__      (						self   )					->     str:
     __a								=       FlaxBeitForImageClassification.from_pretrained('microsoft/beit-base-patch16-224'   )

     __a								=       self.default_image_processor
     __a								=       prepare_img()
     __a								=       image_processor(images=UpperCamelCase   ,				return_tensors='np'   )

     # forward pass
     __a								=       model(**UpperCamelCase   )
     __a								=       outputs.logits

     # verify the logits
     __a								=       (1, 1000)
     self.assertEqual(logits.shape   ,				UpperCamelCase   )

     __a								=       np.array([-1.2_385, -1.0_987, -1.0_108]   )

     self.assertTrue(np.allclose(logits[0, :3]   ,				UpperCamelCase   ,				atol=1e-4   )   )

     __a								=       281
     self.assertEqual(logits.argmax(-1   ).item()   ,				UpperCamelCase   )







   @slow
   def 		UpperCamelCase__      (						self   )					->     Any:
     __a								=       FlaxBeitForImageClassification.from_pretrained('microsoft/beit-large-patch16-224-pt22k-ft22k'   )

     __a								=       self.default_image_processor
     __a								=       prepare_img()
     __a								=       image_processor(images=UpperCamelCase   ,				return_tensors='np'   )

     # forward pass
     __a								=       model(**UpperCamelCase   )
     __a								=       outputs.logits

     # verify the logits
     __a								=       (1, 2_1841)
     self.assertEqual(logits.shape   ,				UpperCamelCase   )

     __a								=       np.array([1.6_881, -0.2_787, 0.5_901]   )

     self.assertTrue(np.allclose(logits[0, :3]   ,				UpperCamelCase   ,				atol=1e-4   )   )

     __a								=       2396
     self.assertEqual(logits.argmax(-1   ).item()   ,				UpperCamelCase   )









'''simple docstring'''


from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup


def     SCREAMING_SNAKE_CASE							(   a_						:				str = ""      ):
  __a								=       url or 'https://www.imdb.com/chart/top/?ref_=nv_mv_250'
  __a								=       BeautifulSoup(requests.get(a_      ).text					,       'html.parser'      )
  __a								=       soup.find_all('td'					,       attrs='titleColumn'      )
  __a								=       soup.find_all('td'					,       class_='ratingColumn imdbRating'      )
  return {
      title.a.text: float(rating.strong.text      )
      for title, rating in zip(a_					,       a_      )
  }


def     SCREAMING_SNAKE_CASE							(   a_						:				str = "IMDb_Top_250_Movies.csv"      ):
  __a								=       get_imdb_top_aaa_movies()
  with open(a_					,       'w'					,       newline=''      ) as out_file:
    __a								=       csv.writer(a_      )
    writer.writerow(['Movie title', 'IMDb rating']      )
    for title, rating in movies.items():
      writer.writerow([title, rating]      )


if __name__ == "__main__":
  write_movies()



